Emissions reduction, military lands, and Canada’s defence policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines how states can reduce their defence-related greenhouse gas (GHG) emissions in the context of a deteriorating international security environment. It examines Canada’s defence policy as a case study for the challenge of reducing military emissions while defence spending and military operations are both increasing. We propose that in addition to other emission reduction measures, Canada should explore increasing carbon sequestration on its 2.2 million hectares of military-owned lands, coastal waters, and adjacent Crown lands. In the context of the polycrisis, sequestering carbon on military lands offers multiple policy “wins” by helping meet emissions reduction targets, enhance biodiversity, and provide opportunities for collaboration with Indigenous communities while contributing to important security needs for Canada and its allies. Canada’s vast size provides a comparative advantage for this approach, but it could also create opportunities for cooperation with allied states, demonstrating the applicability of military carbon sequestration across diverse contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it